Iteratively reweighted least squares (IRLS) is an iterative optimization technique used to solve nonlinear least squares problems. In each iteration, IRLS assigns weights to the data points based on their residuals from the current model fit. These weights are used to adjust the objective function, resulting in a weighted least squares problem that is easier to solve. By iterating between updating the weights and solving the weighted least squares problem, IRLS obtains a solution that minimizes the weighted sum of squared residuals.
